Urinary Tract Infections and Encrustation in Urinary Stents
Bibliographic record
Abstract
Abstract Regardless clinical benefits of urinary stents, these indispensable tools for everyday practice come with substantial hindrances as they can lead to stent-related symptoms, encrustation, hematuria, infection and hence to an overall reduction in the quality of life of patients. Bacterial colonization of foreign bodies has been a significant problem in Medicine in general and Urology in particular for decades. Studies have shown that around 42–100% of all indwelling ureteral stents are colonized by bacteria. Typically, the bacteria continue to form a more mature biofilm as large, structured communities of bacteria adhere onto surfaces and secret polysaccharides, nucleic acids, lipids and proteins that form an eminently protective cast around the bacteria. Due to the complex biology and interactions between foreign body surfaces, the host and microbes, a simple, one-fits-all solution is not very likely to be developed. Nonetheless, our knowledge of the underlying biology has dramatically expanded, and novel technologies are being tested. Probably the easiest solution is to appraise ureteral stenting critically and omit stenting whenever feasible. However, for patients in need of a ureteral stent the future might bring “ideal” stents that are biodegradable, coated to avoid biofilm formation and incrustation and ideally emit sufficient levels of specific drugs that prevent tissue ingrowth or even dissolve urinary calculi.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".